The present invention relates to an automated system for detecting bruises on apples, or similar fruit, which employs an unique image processing technique to distinguish bruised tissue from non-bruised tissue.
Apples are graded according to the amount of bruised skin on their surface. In New York State, for example, apples for processing are reduced in value when greater than 5% of the total apple surface is bruised. Fresh market apples, on the other hand, are graded not only on the size of bruises, but also on the number of bruises. Most apples are graded for size and bruises by U.S.D.A. inspectors who examine a small portion of apples randomly selected from each truck load. Differences in inspector evaluation methods and judgement allow for significant and undesirable variations in the application of the U.S.D.A. standards. What would be desirable then, is for an automated system to be devised which can quickly and accurately determine the amount of bruising on an apple's surface.
In recent years, considerable development work on automated apple grading systems has been conducted. For example, U.S. Pat. No. 3,867,041, to Brown et al., discloses a method for detecting bruises in fruit which employs the use of diffuse surface reflectance of near infrared radiation to differentiate bruised tissue from non-bruised tissue. In ASAE Paper No. 81-3537 (1981), Graf et al. demonstrates this diffuse reflectance detection technique using digital imaging as a means to perform the detection by a non-contact process in a laboratory environment. Experimentation with different image analysis techniques showed that a multivariable linear statistical classification of each pixel in an apple image to be the most successful approach to classify imaged apple tissue bruised or non-bruised. The classification algorithms classified bruised and non-bruised tissue areas as well as a human inspector.
In 1984, R. W. Taylor et al. demonstrated that a digital line scan camera could be used in this system in place of a digital matrix camera (Taylor, R. W., Rehkugler, G. E., Throop, J. A., Apple Bruise Detection Using a Digital Line Scan Camera System, Agricultural Electronics-1983 and Beyond. American Society of Agricultural Engineers, pp. 652-662). This eliminated perspective spatial distortion in one dimension and allowed, with careful camera placement, the elimination of the background in the image. Taylor found that the quadratic statistical classification process used was not robust enough to handle the variability of the data. Due to the lack of manufactured uniformity between pixels, an elaborate, time-consuming process of normalization of pixel response was required to improve this classification process. The conversion of Taylor's algorithms from a quadratic to a linear classifier has failed to improve their performance. Therefore, a non-statistical approach for image analysis for classifying apple tissue and a new algorithm was needed. A suitable algorithm and classification process would be one that gave equal or better results of classification on the same apple images as the algorithm used by Graf et al.